In [1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
iris = pd.read_csv(r"C:\Users\aarav\OneDrive\Pictures\iris\Iris.csv")
In [2]:
print(iris.head())
   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species
0   1            5.1           3.5            1.4           0.2  Iris-setosa
1   2            4.9           3.0            1.4           0.2  Iris-setosa
2   3            4.7           3.2            1.3           0.2  Iris-setosa
3   4            4.6           3.1            1.5           0.2  Iris-setosa
4   5            5.0           3.6            1.4           0.2  Iris-setosa
In [3]:
print(iris.describe())
               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm
count  150.000000     150.000000    150.000000     150.000000    150.000000
mean    75.500000       5.843333      3.054000       3.758667      1.198667
std     43.445368       0.828066      0.433594       1.764420      0.763161
min      1.000000       4.300000      2.000000       1.000000      0.100000
25%     38.250000       5.100000      2.800000       1.600000      0.300000
50%     75.500000       5.800000      3.000000       4.350000      1.300000
75%    112.750000       6.400000      3.300000       5.100000      1.800000
max    150.000000       7.900000      4.400000       6.900000      2.500000
In [4]:
print("Target Labels", iris["Species"].unique())
Target Labels ['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']
In [5]:
import plotly.express as px
fig = px.scatter(iris, x="SepalWidthCm", y="SepalLengthCm", color="Species")
fig.show()
In [7]:
x = iris.drop(['Id', 'Species'], axis=1)
y = iris['Species']

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, 
                                                    test_size=0.2, 
                                                    random_state=0)

from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(x_train, y_train)
Out[7]:
KNeighborsClassifier(n_neighbors=1)
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KNeighborsClassifier(n_neighbors=1)
In [8]:
x_new = np.array([[5, 2.9, 1, 0.2]])
prediction = knn.predict(x_new)
print("Prediction: {}".format(prediction))
Prediction: ['Iris-setosa']
C:\Users\aarav\anaconda3\Lib\site-packages\sklearn\base.py:464: UserWarning:

X does not have valid feature names, but KNeighborsClassifier was fitted with feature names

In [ ]: